R Notebook

rm(list = ls())

Libraries

library(data.table)
library(tidyr)
library(maps)
library(haven)
library(ggplot2)
library(dplyr)
library(readxl)
library(ggrepel)
library(wordcloud)
library(lme4)
library(lmerTest)
library(reshape2)
library(patchwork)
library(psych)

PREP THE DATASET FOR ANALYSIS WVS 5 & 6 ####################

#read the data (Wave 5)

# Data of Wave 5
WV5_data <- readRDS("/Users/laurabazzigher/Documents/GitHub/risk_wvs/data/dataset/WV6_dataset_wave_5_6/F00007944-WV5_Data_R_v20180912.rds")

# Convert WV5_data-object in data.frame 
WV5_data_df <- as.data.frame(WV5_data)

# show first five columns
WV5_data_df

clean the data set

#rename the variables
WV5_data <- WV5_data_df %>%
  rename(gender = V235, age = V237, country_code = V2, wave = V1, risktaking = V86, children = V56, married = V55, employed = V241, education = V238)
WV5_data

colnames(WV5_data)

  [1] "wave"          "V1A"           "V1B"           "country_code"  "V2A"           "V3"            "V4"           
  [8] "V4_CO"         "V5"            "V5_CO"         "V6"            "V6_CO"         "V7"            "V7_CO"        
 [15] "V8"            "V8_CO"         "V9"            "V9_CO"         "V10"           "V11"           "V12"          
 [22] "V13"           "V14"           "V15"           "V16"           "V17"           "V18"           "V19"          
 [29] "V20"           "V21"           "V22"           "V23"           "V24"           "V25"           "V26"          
 [36] "V27"           "V28"           "V29"           "V30"           "V31"           "V32"           "V33"          
 [43] "V34"           "V35"           "V36"           "V37"           "V38"           "V39"           "V40"          
 [50] "V41"           "V42"           "V43"           "V43_01"        "V43_02"        "V43_03"        "V43_04"       
 [57] "V43_05"        "V43_06"        "V43_07"        "V43_08"        "V43_09"        "V43_10"        "V43_11"       
 [64] "V43_12"        "V43_13"        "V43_14"        "V43_15"        "V43_16"        "V43_17"        "V43_18"       
 [71] "V43_19"        "V43_20"        "V43_21"        "V43_22"        "V43_23"        "V43_24"        "V43_25"       
 [78] "V43_26"        "V43_27"        "V43_28"        "V43_29"        "V43_30"        "V44"           "V45"          
 [85] "V46"           "V47"           "V48"           "V49"           "V50"           "V51"           "V52"          
 [92] "V53"           "V54"           "married"       "children"      "V57"           "V58"           "V59"          
 [99] "V60"           "V61"           "V62"           "V63"           "V64"           "V65"           "V66"          
[106] "V67"           "V68"           "V69"           "V69_HK"        "V70"           "V70_HK"        "V71"          
[113] "V72"           "V73"           "V73_HK"        "V74"           "V74_HK"        "V75"           "V76"          
[120] "V77"           "V78"           "V79"           "V80"           "V81"           "V82"           "V83"          
[127] "V84"           "V85"           "risktaking"    "V87"           "V88"           "V89"           "V90"          
[134] "V91"           "V92"           "V93"           "V94"           "V95"           "V96"           "V97"          
[141] "V98"           "V99"           "V100"          "V101"          "V102"          "V103"          "V104"         
[148] "V105"          "V106"          "V107"          "V108"          "V109"          "V110"          "V111"         
[155] "V112"          "V113"          "V114"          "V115"          "V116"          "V117"          "V118"         
[162] "V119"          "V120"          "V121"          "V122"          "V123"          "V124"          "V125"         
[169] "V126"          "V127"          "V128"          "V129"          "V130"          "V130_CA_1"     "V130_IQ_1"    
[176] "V130_IQ_2"     "V130_IQ_3"     "V130_IQ_4"     "V130_NZ_1"     "V130_NZ_2"     "V131"          "V132"         
[183] "V133"          "V134"          "V135"          "V136"          "V137"          "V138"          "V139"         
[190] "V140"          "V141"          "V142"          "V143"          "V144"          "V145"          "V146_00"      
[197] "V146_01"       "V146_02"       "V146_03"       "V146_04"       "V146_05"       "V146_06"       "V146_07"      
[204] "V146_08"       "V146_09"       "V146_10"       "V146_11"       "V146_12"       "V146_13"       "V146_14"      
[211] "V146_15"       "V146_16"       "V146_17"       "V146_18"       "V146_19"       "V146_20"       "V146_21"      
[218] "V146_22"       "V147"          "V148"          "V149"          "V150"          "V151"          "V151_IQ_A"    
[225] "V151_IQ_B"     "V152"          "V153"          "V154"          "V155"          "V156"          "V157"         
[232] "V158"          "V159"          "V160"          "V161"          "V162"          "V163"          "V164"         
[239] "V165"          "V166"          "V167"          "V168"          "V169"          "V170"          "V171"         
[246] "V172"          "V173"          "V174"          "V175"          "V176"          "V177"          "V178"         
[253] "V179"          "V180"          "V181"          "V182"          "V183"          "V184"          "V185"         
[260] "V186"          "V187"          "V188"          "V189"          "V190"          "V191"          "V192"         
[267] "V193"          "V194"          "V195"          "V196"          "V197"          "V198"          "V199"         
[274] "V200"          "V201"          "V202"          "V203"          "V204"          "V205"          "V206"         
[281] "V207"          "V208"          "V209"          "V210"          "V211"          "V212"          "V213A"        
[288] "V213B"         "V213C"         "V213D"         "V213E"         "V213F"         "V213G"         "V213H"        
[295] "V213K"         "V213L"         "V213M"         "V213N"         "V214"          "V215"          "V216"         
[302] "V217"          "V218"          "V219"          "V220"          "V221"          "V222"          "V223"         
[309] "V224"          "V225"          "V226"          "V227"          "V228"          "V229"          "V230"         
[316] "V231"          "V232"          "V233"          "V233A"         "V234"          "gender"        "V236"         
[323] "age"           "education"     "V238CS"        "V239"          "V240"          "employed"      "V242"         
[330] "V242A_CO"      "V243"          "V244"          "V245"          "V246"          "V247"          "V248"         
[337] "V249"          "V250"          "V251"          "V252"          "V252B"         "V253"          "V253CS"       
[344] "V254"          "V255"          "V255CS"        "V256"          "V257"          "V257B"         "V257C"        
[351] "V258"          "V259"          "V259A"         "V260"          "V261"          "V262"          "V263"         
[358] "V264"          "V265"          "S024"          "S025"          "Y001"          "Y002"          "Y003"         
[365] "SACSECVAL"     "SECVALWGT"     "RESEMAVAL"     "WEIGHTB"       "I_AUTHORITY"   "I_NATIONALISM" "I_DEVOUT"     
[372] "DEFIANCE"      "WEIGHT1A"      "I_RELIGIMP"    "I_RELIGBEL"    "I_RELIGPRAC"   "DISBELIEF"     "WEIGHT2A"     
[379] "I_NORM1"       "I_NORM2"       "I_NORM3"       "RELATIVISM"    "WEIGHT3A"      "I_TRUSTARMY"   "I_TRUSTPOLICE"
[386] "I_TRUSTCOURTS" "SCEPTICISM"    "WEIGHT4A"      "I_INDEP"       "I_IMAGIN"      "I_NONOBED"     "AUTONOMY"     
[393] "WEIGHT1B"      "I_WOMJOB"      "I_WOMPOL"      "I_WOMEDU"      "EQUALITY"      "WEIGHT2B"      "I_HOMOLIB"    
[400] "I_ABORTLIB"    "I_DIVORLIB"    "CHOICE"        "WEIGHT3B"      "I_VOICE1"      "I_VOICE2"      "I_VOI2_00"    
[407] "VOICE"         "WEIGHT4B"      "S001"          "S007"          "S018"          "S019"          "S021"         
[414] "COW"          

#select only the variables of interest
WV5_data <- WV5_data %>%
  dplyr::select(gender, age, country_code, wave, risktaking, children, employed, education, married)
WV5_data

Read countrynames data from the CSV file (to decode the dataset 5)

countrynames <- read.csv("/Users/laurabazzigher/Documents/GitHub/risk_wvs/data/dataset/WV6_dataset_wave_5_6/countrynames.txt", header = FALSE, as.is = TRUE)
colnames(countrynames) <- c("code", "name")

# Assuming WV5_data has a column named country_code
WV5_data$country <- countrynames$name[match(WV5_data$country_code, countrynames$code)]

# Check the frequency of each country in the new column
table(WV5_data$country)

            Andorra           Argentina           Australia              Brazil            Bulgaria        Burkina Faso 
               1003                1002                1421                1500                1001                1534 
             Canada               Chile               China            Colombia          Cyprus (G)               Egypt 
               2164                1000                1991                3025                1050                3051 
           Ethiopia             Finland              France             Georgia             Germany               Ghana 
               1500                1014                1001                1500                2064                1534 
      Great Britain           Guatemala           Hong Kong             Hungary               India           Indonesia 
               1041                1000                1252                1007                2001                2015 
               Iran                Iraq               Italy               Japan              Jordan            Malaysia 
               2667                2701                1012                1096                1200                1201 
               Mali              Mexico             Moldova             Morocco         Netherlands         New Zealand 
               1534                1560                1046                1200                1050                 954 
             Norway                Peru              Poland             Romania              Russia              Rwanda 
               1025                1500                1000                1776                2033                1507 
           Slovenia        South Africa         South Korea               Spain              Sweden         Switzerland 
               1037                2988                1200                1200                1003                1241 
             Taiwan            Thailand Trinidad and Tobago              Turkey             Ukraine       United States 
               1227                1534                1002                1346                1000                1249 
            Uruguay            Viet Nam              Zambia 
               1000                1495                1500 

# Display the updated WV5_data
print(WV5_data)

#control wave 5

length(unique(WV5_data$country)) 

[1] 58

nrow(WV5_data) # number of individuals 

[1] 83975

range(WV5_data$age) 

[1] -5 98

table(WV5_data$gender) # sex table(data$sex)/nrow(data) 

   -2     1     2 
   96 40218 43661 

#Read Dataset (Wave 6)

load("/Users/laurabazzigher/Documents/GitHub/risk_wvs/data/dataset/WV6_dataset_wave_5_6/WV6_Data_R_v20201117.rdata") 
WV6_data <- WV6_Data_R_v20201117 

print(WV6_data)

#rename variables

WV6_data <- WV6_data %>%
  rename(wave = V1, gender = V240, age = V242,country_code = V2, risktaking = V76, children = V58, married = V57, employed = V229, education = V248)

#select only the variables of interest
WV6_data <- WV6_data %>%
  dplyr::select(gender, age, country_code, wave, risktaking, children, employed, education, married)
WV6_data

#decode daraset (Wave 6)

countrynames = read.csv("/Users/laurabazzigher/Documents/GitHub/risk_wvs/data/dataset/WV6_dataset_wave_5_6/countrynames.txt", header=FALSE,as.is=TRUE)
colnames(countrynames) = c("code", "name")
WV6_data$country = countrynames$name [match(WV6_data$country_code, countrynames$code)]
table(WV6_data$country)

            Algeria           Argentina             Armenia           Australia          Azerbaijan             Belarus 
               1200                1030                1100                1477                1002                1535 
             Brazil               Chile               China            Colombia          Cyprus (G)             Ecuador 
               1486                1000                2300                1512                1000                1202 
              Egypt             Estonia             Georgia             Germany               Ghana               Haiti 
               1523                1533                1202                2046                1552                1996 
          Hong Kong               India                Iraq               Japan              Jordan          Kazakhstan 
               1000                4078                1200                2443                1200                1500 
             Kuwait          Kyrgyzstan             Lebanon               Libya            Malaysia              Mexico 
               1303                1500                1200                2131                1300                2000 
            Morocco         Netherlands         New Zealand             Nigeria            Pakistan           Palestine 
               1200                1902                 841                1759                1200                1000 
               Peru         Philippines              Poland               Qatar             Romania              Russia 
               1210                1200                 966                1060                1503                2500 
             Rwanda           Singapore            Slovenia        South Africa         South Korea               Spain 
               1527                1972                1069                3531                1200                1189 
             Sweden              Taiwan            Thailand Trinidad and Tobago             Tunisia              Turkey 
               1206                1238                1200                 999                1205                1605 
            Ukraine       United States             Uruguay          Uzbekistan               Yemen            Zimbabwe 
               1500                2232                1000                1500                1000                1500 

WV6_data

#control wave 6

length(unique(WV6_data$country)) 

[1] 60

nrow(WV6_data) # number of individuals  

[1] 89565

range(WV6_data$age, na.rm = TRUE)

[1]  16 102

table(WV6_data$gender) # sex table(data$sex)/nrow(data) 

    1     2 
42723 46751 

#combine the 2 dataset (Wave 6 + Wave 5)

WVS_data = rbind(WV5_data, WV6_data)
WVS_data

length(unique(WVS_data$country)) 

[1] 80

nrow(WVS_data) # number of individuals 

[1] 173540

range(WVS_data$age, na.rm=TRUE) 

[1]  -5 102

table(WVS_data$gender) # sex table(data$sex)/nrow(data) 

   -2     1     2 
   96 82941 90412 

#exclusion of participants and omission of missing data (na)

WVS_data = subset(WVS_data, risktaking > 0 & gender > 0 & age >0 & education > 0 & employed > 0 & married > 0 & children >= 0)
### WVS_data <- na.omit(WVS_data) ### excluded because it is not in code from Mata et al., 2016

# Use the mutate function to change the country name
WVS_data <- WVS_data %>%
  mutate(country = ifelse(country == "Great Britain", "United Kingdom", country))

length(unique(WVS_data$country)) 

[1] 77

nrow(WVS_data) # number of individuals 

[1] 149626

range(WVS_data$age, na.rm=TRUE) 

[1] 15 99

table(WVS_data$gender) # sex table(data$sex)/nrow(data) 

    1     2 
71689 77937 

create a categorical education variable (with fewer categories than the original)

# Neue Spalte 'education_cat' erstellen und initialisieren
WVS_data$education_cat <- NA

# Kategorien zuweisen basierend auf den Bildungsstufen
WVS_data$education_cat <- ifelse(WVS_data$education %in% c(1, 2), "incomplete or no primary education", 
                          ifelse(WVS_data$education %in% c(3, 4, 5, 6), "No Uni",
                          ifelse(WVS_data$education %in% c(7, 8, 9), "Uni", NA)))

# Tabelle der neuen Kategorien anzeigen
table(WVS_data$education_cat)

incomplete or no primary education                             No Uni                                Uni 
                             19354                              70033                              60239 

#Transformation of item risktaking

# Transfrom risk item such that high values represent more risk taking
WVS_data$risktaking = 6 - WVS_data$risktaking + 1

# Transform risk variable into T-score (mean = 50, sd = 10)
WVS_data$T_score_risktaking = 10*scale(WVS_data$risktaking, center=TRUE,scale=TRUE)+50

WVS_data

#Transform risk variable into Z score 
# Assuming T-scores have a mean of 50 and a standard deviation of 10
#WVS_data$Z_score_risktaking = (WVS_data$T_score_risktaking - 50) / 10

# Print the resulting data frame
print(WVS_data)

WVS_data <- WVS_data %>%
  group_by(country) %>%
  mutate(z_score_age = scale(age))
WVS_data

Dichotomizing Variables: This helps estimating and interpreting the models later on…

WVS_data$gender = ifelse(WVS_data$gender == 1, 0, 1) # sex: male vs. female
WVS_data$children = ifelse(WVS_data$children == 0, 0, 1) # children: no vs. yes
WVS_data$married = ifelse(WVS_data$married == 1, 1, 0) # married: yes vs. no
WVS_data$employed = ifelse(WVS_data$employed < 4, 1, 0) # employed: yes vs. no
WVS_data$education = ifelse(WVS_data$education < 4, 0, 1) # education: no primary vs. primary+ 
head(WVS_data)

Control the data

length(unique(WVS_data$country))

[1] 77

nrow(WVS_data)

[1] 149626

range(WVS_data$age)

[1] 15 99

table(WVS_data$gender)

    0     1 
71689 77937 

table(WVS_data$children)

     0      1 
 44434 105192 

table(WVS_data$married)

    0     1 
66354 83272 

table(WVS_data$employed)

    0     1 
69519 80107 

table(WVS_data$education)

     0      1 
 38011 111615 

PREP THE DATASET FOR ANALYSIS HARDSHIP ####################

head(WVS_data)

read in file that contains hardship indicators manually collected from CIA factbook, WHO, and World Bank

csv_path <- "/Users/laurabazzigher/Documents/GitHub/risk_wvs/data/Data_Mata_et_al._2016/countryfacts_selection_new.csv"
countryfacts <- read.csv(csv_path, as.is = TRUE, header = TRUE) 


labels=c("code","country","codeWVS","Homicide","GDP","InfMort","LifeExp","GINI","GenderPEdu")
names(countryfacts)=labels

missings=colSums(is.na(countryfacts[,4:9]))/77 # proportion of missing values for each of the hardship indicators

unique(WVS_data$country_code) %in% countryfacts$codeWVS # check that all countries in the subset of the WVS data are included in the countryfacts file

 [1] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[26] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[51] TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
[76] TRUE TRUE

countryfacts

Controll Countryfacts

# Zeige eindeutige Werte für jede Spalte in countryfacts an
for (col in names(countryfacts)) {
  unique_values <- unique(countryfacts[[col]])
  cat("Unique values for column", col, ":", "\n")
  for (val in unique_values) {
    cat(val, "\n")
  }
  cat("\n")
}

Unique values for column code : 
DZA 
ADO 
AZE 
ARG 
AUS 
BHR 
ARM 
BRA 
BGR 
BLR 
CAN 
CHL 
CHN 
TW 
COL 
CYP 
ECU 
ETH 
EST 
FIN 
FRA 
GEO 
PS 
DEU 
GHA 
HUN 
IND 
IDN 
IRN 
GI 
JPN 
KAZ 
JOR 
KOR 
KWT 
KGZ 
LBN 
LBY 
MYS 
MLI 
MEX 
MDA 
MAR 
NLD 
NZL 
NGA 
NOR 
PAK 
PER 
PHL 
POL 
QAT 
ROM 
RUS 
RWA 
SGP 
VNM 
SVN 
ZAF 
ZWE 
ESP 
SWE 
CHE 
THA 
TTO 
TUN 
TUR 
UKR 
EGY 
UK 
USA 
BFA 
URY 
UZB 
YEM 
SRB 
ZMB 
HTI 

Unique values for column country : 
Algeria 
Andorra 
Azerbaijan 
Argentina 
Australia 
Bahrain 
Armenia 
Brazil 
Bulgaria 
Belarus 
Canada 
Chile 
China 
Taiwan 
Colombia 
Cyprus 
Ecuador 
Ethiopia 
Estonia 
Finland 
France 
Georgia 
Palestine 
Germany 
Ghana 
Hungary 
India 
Indonesia 
Iran 
Gibraltar 
Japan 
Kazakhstan 
Jordan 
South Korea 
Kuwait 
Kyrgyzstan 
Lebanon 
Libya 
Malaysia 
Mali 
Mexico 
Moldova 
Morocco 
Netherlands 
New Zealand 
Nigeria 
Norway 
Pakistan 
Peru 
Philippines 
Poland 
Qatar 
Romania 
Russia 
Rwanda 
Singapore 
Vietnam 
Slovenia 
South Africa 
Zimbabwe 
Spain 
Sweden 
Switzerland 
Thailand 
Trinidad 
Tunisia 
Turkey 
Ukraine 
Egypt 
Great Britain 
USA 
Burkina Faso 
Uruguay 
Uzbekistan 
Yemen 
Serbia 
Zambia 
Haiti 

Unique values for column codeWVS : 
12 
20 
31 
32 
36 
48 
51 
76 
100 
112 
124 
152 
156 
158 
170 
196 
218 
231 
233 
246 
250 
268 
275 
276 
288 
348 
356 
360 
364 
368 
392 
398 
400 
410 
414 
417 
422 
434 
458 
466 
484 
498 
504 
528 
554 
566 
578 
586 
604 
608 
616 
634 
642 
643 
646 
702 
704 
705 
710 
716 
724 
752 
756 
764 
780 
788 
792 
804 
818 
826 
840 
854 
858 
860 
887 
688 
894 
332 

Unique values for column Homicide : 
4.4 
0.8 
2.4 
6 
1.1 
2.1 
32.4 
1.9 
6.2 
1.8 
4.6 
NA 
43.9 
2 
13.8 
8 
5.4 
1.4 
1 
4.8 
10 
1.5 
4.3 
4.7 
0.4 
9.2 
2.9 
3.1 
9.1 
2.6 
11 
22 
7.5 
2.5 
0.9 
1.2 
10.1 
0.6 
8.9 
12.4 
7.1 
13.1 
5.8 
4 
0.7 
35.7 
15.1 
5.5 
35.3 
2.7 
5.2 
5.1 
9.8 
7.9 
3.2 
1.6 
10.5 
20.98444 

Unique values for column GDP : 
7500 
37200 
10800 
18600 
43000 
51400 
6300 
12100 
14400 
16100 
43100 
19100 
9800 
39600 
11100 
24500 
10600 
1300 
22400 
35900 
35700 
6100 
NA 
39500 
3500 
19800 
4000 
5200 
12800 
37100 
14100 
33200 
42100 
2500 
15800 
11300 
17500 
1100 
15600 
3800 
5500 
43300 
30400 
2800 
55400 
3100 
4700 
21100 
102100 
18100 
1500 
62400 
27400 
11500 
600 
30100 
40900 
54800 
9900 
20300 
15300 
7400 
6600 
37300 
52800 
16600 
1800 
2900 

Unique values for column InfMort : 
21.76 
3.69 
26.67 
9.96 
4.43 
9.68 
13.97 
19.21 
15.08 
3.64 
4.71 
7.02 
14.79 
4.49 
15.02 
8.54 
17.93 
55.77 
6.7 
3.36 
3.31 
16.68 
NA 
3.46 
38.52 
5.09 
43.19 
25.16 
39 
6.29 
2.13 
21.61 
15.73 
3.93 
7.51 
28.71 
7.98 
11.87 
13.69 
104.34 
12.58 
12.93 
24.52 
3.66 
4.59 
74.09 
2.48 
57.48 
20.21 
17.64 
6.19 
6.42 
10.16 
7.08 
59.59 
2.53 
18.99 
4.04 
41.61 
26.55 
3.33 
2.6 
3.73 
9.86 
24.82 
23.19 
21.43 
8.1 
22.41 
4.4 
6.17 
76.8 
8.97 
19.84 
50.41 
6.16 
66.62 
41.1 

Unique values for column LifeExp : 
76.39 
82.65 
71.91 
77.51 
82.07 
78.58 
74.12 
73.28 
74.33 
72.15 
81.67 
78.44 
75.15 
79.84 
75.25 
78.34 
76.36 
60.75 
74.07 
79.69 
81.66 
75.72 
NA 
80.44 
65.75 
75.46 
67.8 
72.17 
70.89 
79.13 
84.46 
70.24 
74.1 
79.8 
77.64 
70.06 
77.22 
76.04 
74.52 
54.95 
75.43 
70.12 
76.51 
81.12 
80.93 
52.62 
81.6 
67.05 
73.23 
72.48 
76.65 
78.38 
74.69 
70.16 
59.26 
84.38 
72.91 
77.83 
49.56 
55.68 
81.47 
81.89 
82.39 
74.18 
72.29 
75.68 
73.29 
69.14 
73.45 
80.42 
79.56 
54.78 
76.81 
64.83 
75.02 
51.83 
38.78 

Unique values for column GINI : 
35.3 
NA 
33.7 
45.8 
30.3 
30.9 
51.9 
45.3 
27.2 
32.1 
52.1 
47.3 
34.2 
53.5 
32.4 
48.5 
33 
31.3 
26.8 
46 
27 
39.4 
24.7 
36.8 
44.5 
37.6 
28.9 
39.7 
31.1 
33.4 
46.2 
40.1 
48.3 
40.9 
25.1 
36.2 
43.7 
25 
29.6 
44.8 
34.1 
27.4 
42 
46.8 
46.3 
23.7 
63.1 
50.1 
34 
23 
28.7 
40 
40.2 
28.2 
30.8 
32.3 
45 
39.5 
37.7 
38 
57.5 
66.3 

Unique values for column GenderPEdu : 
94.426 
NA 
97.948 
98.743 
99.513 
103.237 
114.144 
94.182 
99.191 
100.243 
100.678 
96.873 
99.907 
98.117 
99.77 
100.307 
86.105 
100.649 
99.471 
100.044 
101.032 
99.574 
99.883 
98.939 
102.298 
100.046 
101.797 
99.83 
101.458 
98.242 
98.98 
98.716 
98.175 
92.021 
96.044 
93.895 
88.371 
100.231 
100.068 
95.185 
99.051 
100.474 
91.588 
100.45 
87.071 
99.122 
97.843 
100.376 
90.42 
98.532 
100.808 
102.225 
97.829 
100.203 
94.856 
98.614 
99.35 
99.61 
100 
95.254 
96.603 
97.167 
98.551 
101.884 
96.003 
98.38 
96.558 
96.746 
97.046 
84.034 
100.093 
99.196 
57.37 

Plot histogram of all hardship indicators

# Plot histogram of all hardship indicators
combined_plot <- NULL # Leeres Plot-Objekt erstellen

# Define the vector of labels for the items
items <- c("Homicide","GDP","InfMort","LifeExp","GINI","GenderPEdu")

# Loop durch jedes Item und füge das Histogramm zum kombinierten Plot hinzu
for (item in items) {
  # Erstelle ein Histogramm für das aktuelle Item
  plot <- ggplot(countryfacts, aes_string(x = item)) +
    geom_histogram(binwidth = 1, fill = "skyblue", color = "black") +
    labs(title = paste(item),
         x = item,
         y = "Frequency") +
    theme_minimal()
  
  # Füge das Histogramm zum kombinierten Plot hinzu
  if (is.null(combined_plot)) {
    combined_plot <- plot
  } else {
    combined_plot <- combined_plot + plot
  }
}

# Zeige den kombinierten Plot an
combined_plot

log transform

countryfacts$Homicide=log(countryfacts$Homicide)
countryfacts$GDP=log(countryfacts$GDP)
countryfacts$InfMort=log(countryfacts$InfMort)
countryfacts$LifeExp=log(countryfacts$LifeExp)
#countryfacts$GINI=log(countryfacts$GINI) # not transformed
countryfacts$GenderPEdu=log(countryfacts$GenderPEdu)

countryfacts

changing variables into the same direction

# Reverse Codierung
countryfacts$Homicide=scale(countryfacts$Homicide)
countryfacts$GDP=scale(-countryfacts$GDP)
countryfacts$InfMort=scale(countryfacts$InfMort)
countryfacts$LifeExp=scale(-countryfacts$LifeExp)
countryfacts$GINI=scale(countryfacts$GINI)
countryfacts$GenderPEdu=scale(-countryfacts$GenderPEdu)

countryfacts

Create the ‘hardship’ column in the ‘hardship’ data frame

countryfacts$hardship <- rowMeans(countryfacts[, c("Homicide", "GDP", "GINI", "LifeExp", "InfMort", "GenderPEdu")], na.rm = TRUE)

countryfacts

Plot histogram of all hardship indicators after log transform

# Plot histogram of all hardship indicators after log transform
# Leeres Plot-Objekt erstellen
combined_plot <- NULL

# Define the vector of labels for the items
items <- c("Homicide", "GDP", "GINI", "LifeExp", "InfMort", "GenderPEdu", "hardship")

# Loop durch jedes Item und füge das Histogramm zum kombinierten Plot hinzu
for (item in items) {
  # Erstelle ein Histogramm für das aktuelle Item
  plot <- ggplot(countryfacts, aes_string(x = item)) +
    geom_histogram(binwidth = 1, fill = "skyblue", color = "black") +
    labs(title = paste(item),
         x = item,
         y = "Frequency") +
    theme_minimal()
  
  # Füge das Histogramm zum kombinierten Plot hinzu
  if (is.null(combined_plot)) {
    combined_plot <- plot
  } else {
    combined_plot <- combined_plot + plot
  }
}

# Zeige den kombinierten Plot an
combined_plot

SUP MATERIALS:Correlation between hardship indicators

heat map

panel.cor = function(x, y, digits = 2, cex.cor, ...)
{
  usr = par("usr"); on.exit(par(usr))
  par(usr = c(0, 1, 0, 1))
  # correlation coefficient
  r = cor(x, y,use="complete.obs")
  txt = format(c(r, 0.123456789), digits = digits)[1]
  txt = paste("r= ", txt, sep = "")
  text(0.5, 0.6, txt)
  
  # p-value calculation
  p = cor.test(x, y,use="complete.obs")$p.value
  txt2 = format(c(p, 0.123456789), digits = digits)[1]
  txt2 = paste("p= ", txt2, sep = "")
  if(p<0.01) txt2 = paste("p ", "<0.01", sep = "")
  text(0.5, 0.4, txt2)
}
pairs(countryfacts[,4:10], upper.panel = panel.cor,las=1,cex.labels=.9) 

dev.print(postscript,"scatter_indicators.eps",width=8, height=8,horizontal=FALSE,onefile=FALSE)

quartz_off_screen 
                2 

Cronbach’s Alpha for Hardship Indicators

library(psych)

# Subset der Hardship-Indikatoren aus dem countryfacts-Datensatz auswählen
hardship_subset <- countryfacts[, c("Homicide", "GDP", "InfMort", "LifeExp", "GINI", "GenderPEdu")]

# Cronbach's Alpha berechnen
alpha_result <- alpha(hardship_subset)

Number of categories should be increased  in order to count frequencies. 

alpha_result

Reliability analysis   
Call: alpha(x = hardship_subset)

 

    95% confidence boundaries 

 Reliability if an item is dropped:

 Item statistics 

# Ersetzen Sie "USA" durch "United States" im countryfacts-Datensatz
countryfacts$country[countryfacts$country == "USA"] <- "United States"

Merge the data matrix with the countryfacts dataset based on the country code

# Merge data from WVS_data and countryfacts
data <- merge(WVS_data, countryfacts[, c("codeWVS", "country", "Homicide", "GDP", "InfMort", "LifeExp", "GINI", "GenderPEdu", "hardship")], 
              by.x = "country_code", by.y = "codeWVS", all.x = TRUE)

# Remove the redundant column 'country_code'
data <- data[, -which(names(data) == "country_code")]

# Set USA as the reference country
if ("United States" %in% data$country.x) {
  data$country <- factor(data$country.x, levels = c("United States", unique(data$country.x)[!data$country.x %in% "United States"]))
} else {
  # If "United States" is not found, keep the country variable as is
  print("Warning: 'United States' not found in the country variable.")
}

Error in `levels<-`(`*tmp*`, value = as.character(levels)) : 
  factor level [72] is duplicated

# List all unique countries in the WVS_data dataset
unique_countries <- unique(data$country)

# Display the list of unique countries
print(unique_countries)

NULL

PREP THE DATASET FOR ANALYSIS MIXED-MODELS ####################

#Add Hardship to WVS_data

head(WVS_data)

Combine WVS_data with the hardship

WVS_mixed_model <- left_join(WVS_data, hardship, by = "country")
WVS_mixed_model

Add Hardship to gps_data

gps_mixed_model <- left_join(gps_data, hardship, by = "country")
gps_mixed_model

MIXED-MODELS #################### #################### ####################

MIXED-MODELS WVS-DATA ####################

Mixed-model WVS - Replication of Mata et al., 2016

intercept only model

model0 = lmer(risktaking ~ 1 + (1|country),data = WVS_mixed_model)
summary_model0=summary(model0)

age, sex

model1 <- lmer(risktaking ~ 1 + scale(z_score_age) + factor(gender) + (1 + scale(z_score_age) + factor(gender) | country), 
               data = WVS_mixed_model, 
                      control = lmerControl(optimizer = "bobyqa"))

Koeffizientenübersicht des Modells anzeigen

print(summary_model1)

# Zusammenfassung des Modells anzeigen
summary_model1 <- summary(model1)

# Gewünschte Werte extrahieren und formatieren
results_model1 <- data.frame(
  Predictor = c("Intercept", "Age", "Gender"),
  Estimate = c(summary_model1$coefficients["(Intercept)", "Estimate"],
               summary_model1$coefficients["scale(z_score_age)", "Estimate"],
               summary_model1$coefficients["factor(gender)1", "Estimate"]),
  SE = c(summary_model1$coefficients["(Intercept)", "Std. Error"],
          summary_model1$coefficients["scale(z_score_age)", "Std. Error"],
          summary_model1$coefficients["factor(gender)1", "Std. Error"]),
  T_score = c(summary_model1$coefficients["(Intercept)", "t value"],
              summary_model1$coefficients["scale(z_score_age)", "t value"],
              summary_model1$coefficients["factor(gender)1", "t value"]),
  p_value = c(summary_model1$coefficients["(Intercept)", "Pr(>|t|)"],
              summary_model1$coefficients["scale(z_score_age)", "Pr(>|t|)"],
              summary_model1$coefficients["factor(gender)1", "Pr(>|t|)"])
)

# Formatierung der p-Werte
results_model1$p_value <- ifelse(results_model1$p_value < 0.001, "< .001", sprintf("%.3f", results_model1$p_value))

# Ergebnisse anzeigen
print(results_model1)

age, sex, and covariates (children, marital status, employement status, education)

model2 = lmer(risktaking ~ 1+scale(z_score_age)+factor(gender) + factor(children) + factor(married) + factor(employed) + factor(education)+ (1+scale(z_score_age)+factor(gender)+ factor(children) + factor(married) + factor(employed) + factor(education)|country),data = WVS_mixed_model,control=lmerControl(optCtrl=list(maxfun=30000),optimizer="bobyqa"))
summary_model2=summary(model2)

Koeffizientenübersicht des Modells anzeigen

print(summary_model2)

# Zusammenfassung des Modells anzeigen
summary_model2 <- summary(model2)

# Gewünschte Werte extrahieren und formatieren
results_model2 <- data.frame(
  Predictor = c("Intercept", "Age", "Gender", "Parental status", "Marital status", "Occupational status", "Education"),
  Estimate = c(summary_model2$coefficients["(Intercept)", "Estimate"],
               summary_model2$coefficients["scale(z_score_age)", "Estimate"],
               summary_model2$coefficients["factor(gender)1", "Estimate"],
               summary_model2$coefficients["factor(children)1", "Estimate"],
               summary_model2$coefficients["factor(married)1", "Estimate"],
               summary_model2$coefficients["factor(employed)1", "Estimate"],
               summary_model2$coefficients["factor(education)1", "Estimate"]),
  SE = c(summary_model2$coefficients["(Intercept)", "Std. Error"],
          summary_model2$coefficients["scale(z_score_age)", "Std. Error"],
          summary_model2$coefficients["factor(gender)1", "Std. Error"],
          summary_model2$coefficients["factor(children)1", "Std. Error"],
          summary_model2$coefficients["factor(married)1", "Std. Error"],
          summary_model2$coefficients["factor(employed)1", "Std. Error"],
          summary_model2$coefficients["factor(education)1", "Std. Error"]),
  T_score = c(summary_model2$coefficients["(Intercept)", "t value"],
              summary_model2$coefficients["scale(z_score_age)", "t value"],
              summary_model2$coefficients["factor(gender)1", "t value"],
              summary_model2$coefficients["factor(children)1", "t value"],
              summary_model2$coefficients["factor(married)1", "t value"],
              summary_model2$coefficients["factor(employed)1", "t value"],
              summary_model2$coefficients["factor(education)1", "t value"]),
  p_value = c(summary_model2$coefficients["(Intercept)", "Pr(>|t|)"],
              summary_model2$coefficients["scale(z_score_age)", "Pr(>|t|)"],
              summary_model2$coefficients["factor(gender)1", "Pr(>|t|)"],
              summary_model2$coefficients["factor(children)1", "Pr(>|t|)"],
              summary_model2$coefficients["factor(married)1", "Pr(>|t|)"],
              summary_model2$coefficients["factor(employed)1", "Pr(>|t|)"],
              summary_model2$coefficients["factor(education)1", "Pr(>|t|)"])
)

# Formatierung der p-Werte
results_model2$p_value <- ifelse(results_model2$p_value < 0.001, "< .001", sprintf("%.3f", results_model2$p_value))

# Ergebnisse anzeigen
print(results_model2)

model3 <- lmer(risktaking ~ 1+scale(z_score_age)*hardship+factor(gender)*hardship + factor(children) + factor(married) + factor(employed) + factor(education)+ (1+scale(z_score_age)+factor(gender)+ factor(children) + factor(married) + factor(employed) + factor(education)|country),data = WVS_mixed_model,control=lmerControl(optCtrl=list(maxfun=30000),optimizer="bobyqa"),REML = FALSE)
summary_model3=summary(model3)

Koeffizientenübersicht des Modells anzeigen

print(summary_model3)

Zusammenfassung des Modells anzeigen

# Zusammenfassung des Modells anzeigen
summary_model3 <- summary(model3)

# Gewünschte Werte extrahieren und formatieren
results_model3 <- data.frame(
  Predictor = c("Intercept", "Age", "Gender", "Parental status", "Marital status", "Occupational status", "Education", "Hardship", "Interaction: Gender * Hardship"),
  Estimate = c(summary_model3$coefficients["(Intercept)", "Estimate"],
               summary_model3$coefficients["scale(z_score_age)", "Estimate"],
               summary_model3$coefficients["factor(gender)1", "Estimate"],
               summary_model3$coefficients["factor(children)1", "Estimate"],
               summary_model3$coefficients["factor(married)1", "Estimate"],
               summary_model3$coefficients["factor(employed)1", "Estimate"],
               summary_model3$coefficients["factor(education)1", "Estimate"],
               summary_model3$coefficients["hardship", "Estimate"],
               summary_model3$coefficients["hardship:factor(gender)1", "Estimate"]),
  SE = c(summary_model3$coefficients["(Intercept)", "Std. Error"],
          summary_model3$coefficients["scale(z_score_age)", "Std. Error"],
          summary_model3$coefficients["factor(gender)1", "Std. Error"],
          summary_model3$coefficients["factor(children)1", "Std. Error"],
          summary_model3$coefficients["factor(married)1", "Std. Error"],
          summary_model3$coefficients["factor(employed)1", "Std. Error"],
          summary_model3$coefficients["factor(education)1", "Std. Error"],
          summary_model3$coefficients["hardship", "Std. Error"],
          summary_model3$coefficients["hardship:factor(gender)1", "Std. Error"]),
  T_score = c(summary_model3$coefficients["(Intercept)", "t value"],
              summary_model3$coefficients["scale(z_score_age)", "t value"],
              summary_model3$coefficients["factor(gender)1", "t value"],
              summary_model3$coefficients["factor(children)1", "t value"],
              summary_model3$coefficients["factor(married)1", "t value"],
              summary_model3$coefficients["factor(employed)1", "t value"],
              summary_model3$coefficients["factor(education)1", "t value"],
              summary_model3$coefficients["hardship", "t value"],
              summary_model3$coefficients["hardship:factor(gender)1", "t value"]),
  p_value = c(summary_model3$coefficients["(Intercept)", "Pr(>|t|)"],
              summary_model3$coefficients["scale(z_score_age)", "Pr(>|t|)"],
              summary_model3$coefficients["factor(gender)1", "Pr(>|t|)"],
              summary_model3$coefficients["factor(children)1", "Pr(>|t|)"],
              summary_model3$coefficients["factor(married)1", "Pr(>|t|)"],
              summary_model3$coefficients["factor(employed)1", "Pr(>|t|)"],
              summary_model3$coefficients["factor(education)1", "Pr(>|t|)"],
              summary_model3$coefficients["hardship", "Pr(>|t|)"],
              summary_model3$coefficients["hardship:factor(gender)1", "Pr(>|t|)"])
)

# Formatierung der p-Werte
results_model3$p_value <- ifelse(results_model3$p_value < 0.001, "< .001", sprintf("%.3f", results_model3$p_value))

# Ergebnisse anzeigen
print(results_model3)

anova(model0,model1)
anova(model1,model2)
anova(model2,model3) 

coefsallmodels=rbind(summary_model1$coefficients,
summary_model2$coefficients,
summary_model3$coefficients[c(1:2,4:8,3,9:10),])

write.csv(coefsallmodels,"coefsallmodels.csv")

Delete when submitting the code –> just for me to know where the document is

file_path <- file.path(getwd(), "coefsallmodels.csv")
file_path

# Extrahieren der Koeffizienten-Tabelle für jedes Modell
coefficients_model0 <- summary(model0)$coefficients
coefficients_model1 <- summary(model1)$coefficients
coefficients_model2 <- summary(model2)$coefficients
coefficients_model3 <- summary(model3)$coefficients

# Filtern der erforderlichen Zeilen aus den Koeffizienten
coefficients_model0 <- coefficients_model0[rownames(coefficients_model0) %in% c("(Intercept)", "scale(z_score_age)", "factor(gender)"), ]
coefficients_model1 <- coefficients_model1[rownames(coefficients_model1) %in% c("(Intercept)", "scale(z_score_age)", "factor(gender)"), ]
coefficients_model2 <- coefficients_model2[rownames(coefficients_model2) %in% c("(Intercept)", "scale(z_score_age)", "factor(gender)", "factor(children)", "factor(married)", "factor(employed)", "factor(education)"), ]
coefficients_model3 <- coefficients_model3[rownames(coefficients_model3) %in% c("(Intercept)", "scale(z_score_age)", "factor(gender)", "factor(children)", "factor(married)", "factor(employed)", "factor(education)", "hardship", "scale(z_score_age):hardship", "factor(gender):hardship"), ]

# Zusammenführen der geschätzten Koeffizienten aus allen Modellen
coefs_all_models <- rbind(coefficients_model0, coefficients_model1, coefficients_model2, coefficients_model3)

# Erstellen einer Tabelle aus den Koeffizienten
results_table <- data.frame(
  Predictor = rownames(coefs_all_models),
  b = coefs_all_models[, "Estimate"],
  SE = coefs_all_models[, "Std. Error"],
  T_score = coefs_all_models[, "t value"],
  p_value = coefs_all_models[, "Pr(>|t|)"]
)

# Drucken der Ergebnistabelle
results_table